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Design and Analysis of Time Series Experiments presents the
elements of statistical time series analysis while also addressing
recent developments in research design and causal modeling. A
distinguishing feature of the book is its integration of design and
analysis of time series experiments. Drawing examples from
criminology, economics, education, pharmacology, public policy,
program evaluation, public health, and psychology, Design and
Analysis of Time Series Experiments is addressed to researchers and
graduate students in a wide range of behavioral, biomedical and
social sciences. Readers learn not only how-to skills but, also the
underlying rationales for the design features and the analytical
methods. ARIMA algebra, Box-Jenkins-Tiao models and model-building
strategies, forecasting, and Box-Tiao impact models are developed
in separate chapters. The presentation of the models and
model-building assumes only exposure to an introductory statistics
course, with more difficult mathematical material relegated to
appendices. Separate chapters cover threats to statistical
conclusion validity, internal validity, construct validity, and
external validity with an emphasis on how these threats arise in
time series experiments. Design structures for controlling the
threats are presented and illustrated through examples. The
chapters on statistical conclusion validity and internal validity
introduce Bayesian methods, counterfactual causality and synthetic
control group designs. Building on the earlier of the authors,
Design and Analysis of Time Series Experiments includes more recent
developments in modeling, and considers design issues in greater
detail than any existing work. Additionally, the book appeals to
those who want to conduct or interpret time series experiments, as
well as to those interested in research designs for causal
inference.
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